A cell's DNA-based genetic code is first read into RNA through the highly regulated process of transcription. How transcription is regulated during development or disease progression is therefore a fundamental question in biology that has important implications for basic research and for biomedical research and applications. Accurate and precise knowledge of the location and the intensity of RNA production in the genome and how these change during various cellular responses is the first step in understanding mechanisms of transcription regulation. Two major challenges to overcome when measuring RNA production are the background signal from pre-existing RNAs within the cell, and the deterministic and stochastic cell-to-cell variability in RNA production that can obscure the true transcriptional signatures when examining populations of cells. We propose an approach that combines the expertise of the laboratories of Leighton Core and Suzanne Gaudet to eliminate these sources of error in the measurements of RNA production. The approach has two main components: 1) re-engineer a technique that directly measures new transcription events independently of background signal such that it can be used with small cell populations, and 2) test the efficiency of the modified technique to detect changes during a well-studied cellular response in pure populations of cells that are first sorted by their level of response. Successful execution of the first aim will result in an assay of RNA production that can be much more easily used by a wider range of scientists and applied to a broader array of experimental systems, in particular to rare patient samples where the sample size is limited. In addition to the technical innovations brought about by the first aim, our second aim will reveal the extent to which genome-wide transcription is coordinated within cells and between cells in a population during response to stimuli. From the perspective of better understanding the regulation of transcriptional programs in response to stimuli, we will be able to ascertain whether most of variability in a particular gene's response is independent or coordinated with that of other genes. While our exploratory experiments will focus on insights for the response of a cell line model of T-cells to tumor necrosis factor, the re-engineered approach to measurements of RNA production that we develop will be broadly applicable to most biological experimental systems.